import keras
from keras.models import Sequential
from keras.layers import Dense,Activation,Dropout
import numpy as np
#Generate dummy data
x_train=np.random.random((1000,20))
y_train=np.random.randint(2,size=(1000,1))
x_test=np.random.random((100,20))
y_test=np.random.randint(2,size=(100,1))

model=Sequential()
model.add(Dense(64,input_dim=20,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(64,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1,activation='sigmoid'))

model.compile(loss='binary_crossentropy',optimizer='rmsprop',metrics=['accuracy'])

model.fit(x_train,y_train,epochs=200,batch_size=128)
score=model.evaluate(x_test,y_test,batch_size=128)
print(score)